Conventional approaches for detecting outliers in a dataset involve manual or brute-force analysis of the dataset to identify optimal combinations of features, algorithms, algorithm parameters, or the like for detecting the outliers. Navigating the available choices of features, algorithms, algorithm parameters, or the like and assessing the trade-offs associated with such choices can be a daunting task using conventional approaches to outlier detection, particularly when operating under time and budget constraints. Technical solutions that address at least some of the drawbacks associated with traditional approaches to outlier detection are described herein.